The main advantage of optical correlators, as compared with their digital counterparts, is that high resolution Fourier transform operation on the input optical images may be rapidly executed, typically in nanoseconds, by simply transmitting the input image through a single lens. However, the overall speed of an optical correlator is still limited by how fast the information can be updated on the input devices (e.g., spatial light modulators), the real-time holographic material, and the output device (e.g., camera or detector array). The speeds of these three components are equally important, because the slowest component will determine the overall speed of the system.
Photorefractive crystals have been used as the real-time holographic material. While photorefractive crystals are in general slower than other non-linear optical materials, they can operate with a much lower power requirement. Photorefractive semiconductor materials such as GaAs, InP, and CdTe are generally one to two orders of magnitude faster than photorefractive oxides such as BaTiO.sub.3, (Sr,Ba)NbO.sub.3 (SBN), and Bi.sub.12 SiO.sub.20 (BSO).
In the field of optical correlators, a real-time optical correlator using a photorefractive GaAs crystal and two liquid crystal television (LCTV)-based spatial light modulators has been demonstrated by several investigators. In this correlator, when the shape, size, and orientation of the object in the input image and the object in the reference image are the same, the correlator displays a bright spot (autocorrelation peak) in the output image at an equivalent location of the object in the input image. Therefore, the autocorrelation peak can be used not only to identify an object but also to track its location.
This optical correlator can potentially operate at a frame rate of &gt;1,000 frames/sec, provided other parts of the system have comparable speeds. The speed bottleneck of the optical correlator is at both the input and the output devices.
Furthermore, because both the input and reference images are automatically edge-enhanced in the correlator, the profile of the autocorrelator peak is sharper and most background caused by the clutters are reduced.
However, while the prior art optical correlators can detect and recognize objects, they cannot distinguish whether the object is stationary or moving. Further, if there is background noise that introduces clutter, the prior art optical correlators cannot easily separate the object from the noise.
Thus, there exists a need for a pattern recognition system with the ability to (1) detect and recognizing moving objects and (2) suppress stationary background or stationary clutter.